Based on the power of the computer and on the ability to build adequate models of reality, the simulation of experiments has become an increasingly effective and often superior substitute for physical experimentation. For example, the building and testing of engineering prototypes is an experimentation effort that is done more and more in terms of models and simulations rather than conventionally.
Concurrent Simulation (CS) is the simultaneous, side-by-side simulation of related experiments within one computer run. CS is a method that runs on conventional computers, performing concurrent experiments without concurrent hardware. It applies and is limited to systems simulated with discrete events. Typically 10 to 1,000 times faster than serial (one-at-a-time) simulation of single experiments, its speed is largely based on the number and similarities between experiments. CS dates from 1970/1973 and was first developed for fault simulation of gate-level digital networks. Over the years it has increased in generality and, more recently, evolved into a simulation methodology. Whenever discrete event simulation is the method chosen to solve a particular problem, CS is usually better than serial simulation. CS has several advantages over serial simulation.
First, all experiments advance synchronously through the dimension of time, and CS is therefore analogous to a race in which the experiments are competitors. This constitutes a race methodology and a comparative style of simulation. This methodology and the speed of CS permit the solution of problems more difficult and larger than with serial simulation. A simulation strategy based on this methodology and comparative style is to simulate and observe related experiments which are initially the same but later become different.
Second, observation, which is awkward and costly for serial simulation, is handled easily and elegantly with CS. The experiments are observed comparatively, and can be compared in exact detail as well statistically. Statistical "signatures" are maintained and periodically analyzed for all experiments.
Next, CS offers speed in various forms. Relative to serial simulation, experiments are compressed into a single run. The idle time between serial simulations is avoided and a simulation project is strategically accelerated. Also, due to the number of concurrent experiments, due to their similarity, and the similarity between them and a reference experiment, the CPU time, as mentioned previously, is typically 10 to 1,000 times less than the equivalent serial simulations. And, based on the analysis of signatures, the initial reference experiment may often be replaced with a more central one. This reduces the average differences between reference and concurrent experiments and gains additional speed.
Lastly, CS provides accuracy and generality. In fields such as biology and chemistry it is desirable to perform related and similar physical experiments in parallel, but it is normally too costly due to labor, space, equipment, and the raw materials that are needed. CS is a parallel (and precisely time-synchronous) form of experimentation, and is therefore an alternative to parallel physical experimentation. It requires no resources except a conventional computer and modeling/simulation skills.